Sentiment Analysis and Emotion Detection on Cryptocurrency Related Tweets Using Ensemble LSTM-GRU Model
The cryptocurrency market has been developed at an unprecedented speed over the past few years. Cryptocurrency works similar to standard currency, however, virtual payments are made for goods and services without the intervention of any central authority. Although cryptocurrency ensures legitimate a...
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9751065/ |
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| author | Naila Aslam Furqan Rustam Ernesto Lee Patrick Bernard Washington Imran Ashraf |
| author_facet | Naila Aslam Furqan Rustam Ernesto Lee Patrick Bernard Washington Imran Ashraf |
| author_sort | Naila Aslam |
| collection | DOAJ |
| description | The cryptocurrency market has been developed at an unprecedented speed over the past few years. Cryptocurrency works similar to standard currency, however, virtual payments are made for goods and services without the intervention of any central authority. Although cryptocurrency ensures legitimate and unique transactions by utilizing cryptographic methods, this industry is still in its inception and serious concerns have been raised about its use. Analysis of the sentiments about cryptocurrency is highly desirable to provide a holistic view of peoples’ perceptions. In this regard, this study performs both sentiment analysis and emotion detection using the tweets related to the cryptocurrency which are widely used for predicting the market prices of cryptocurrency. For increasing the efficacy of the analysis, a deep learning ensemble model LSTM-GRU is proposed that combines two recurrent neural networks applications including long short term memory (LSTM) and gated recurrent unit (GRU). LSTM and GRU are stacked where the GRU is trained on the features extracted by LSTM. Utilizing term frequency-inverse document frequency, word2vec, and bag of words (BoW) features, several machine learning and deep learning approaches and a proposed ensemble model are investigated. Furthermore, TextBlob and Text2Emotion are studied for emotion analysis with the selected models. Comparatively, a larger number of people feel happy with the use of cryptocurrency, followed by fear and surprise emotions. Results suggest that the performance of machine learning models is comparatively better when BoW features are used. The proposed LSTM-GRU ensemble shows an accuracy of 0.99 for sentiment analysis, and 0.92 for emotion prediction and outperforms both machine learning and state-of-the-art models. |
| format | Article |
| id | doaj-art-accef814ed23471eb7fea8138aedda44 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-accef814ed23471eb7fea8138aedda442025-08-20T02:10:01ZengIEEEIEEE Access2169-35362022-01-0110393133932410.1109/ACCESS.2022.31656219751065Sentiment Analysis and Emotion Detection on Cryptocurrency Related Tweets Using Ensemble LSTM-GRU ModelNaila Aslam0Furqan Rustam1https://orcid.org/0000-0001-8403-1047Ernesto Lee2https://orcid.org/0000-0002-1209-8565Patrick Bernard Washington3https://orcid.org/0000-0002-3596-9167Imran Ashraf4https://orcid.org/0000-0002-8271-6496School of Electronics and Information Engineering, Hebei University of Technology, Tianjin, ChinaDepartment of Software Engineering, School of Systems and Technology, University of Management and Technology, Lahore, PakistanDepartment of Computer Science, Broward College, Broward County, Fort Lauderdale, FL, USADivision of Business Administration and Economics, Morehouse College, Atlanta, GA, USADepartment of Information and Communication Engineering, Yeungnam University, Gyeongsan-si, South KoreaThe cryptocurrency market has been developed at an unprecedented speed over the past few years. Cryptocurrency works similar to standard currency, however, virtual payments are made for goods and services without the intervention of any central authority. Although cryptocurrency ensures legitimate and unique transactions by utilizing cryptographic methods, this industry is still in its inception and serious concerns have been raised about its use. Analysis of the sentiments about cryptocurrency is highly desirable to provide a holistic view of peoples’ perceptions. In this regard, this study performs both sentiment analysis and emotion detection using the tweets related to the cryptocurrency which are widely used for predicting the market prices of cryptocurrency. For increasing the efficacy of the analysis, a deep learning ensemble model LSTM-GRU is proposed that combines two recurrent neural networks applications including long short term memory (LSTM) and gated recurrent unit (GRU). LSTM and GRU are stacked where the GRU is trained on the features extracted by LSTM. Utilizing term frequency-inverse document frequency, word2vec, and bag of words (BoW) features, several machine learning and deep learning approaches and a proposed ensemble model are investigated. Furthermore, TextBlob and Text2Emotion are studied for emotion analysis with the selected models. Comparatively, a larger number of people feel happy with the use of cryptocurrency, followed by fear and surprise emotions. Results suggest that the performance of machine learning models is comparatively better when BoW features are used. The proposed LSTM-GRU ensemble shows an accuracy of 0.99 for sentiment analysis, and 0.92 for emotion prediction and outperforms both machine learning and state-of-the-art models.https://ieeexplore.ieee.org/document/9751065/Cryptocurrencysentiment analysisText2Emotionemotion analysismachine learning |
| spellingShingle | Naila Aslam Furqan Rustam Ernesto Lee Patrick Bernard Washington Imran Ashraf Sentiment Analysis and Emotion Detection on Cryptocurrency Related Tweets Using Ensemble LSTM-GRU Model IEEE Access Cryptocurrency sentiment analysis Text2Emotion emotion analysis machine learning |
| title | Sentiment Analysis and Emotion Detection on Cryptocurrency Related Tweets Using Ensemble LSTM-GRU Model |
| title_full | Sentiment Analysis and Emotion Detection on Cryptocurrency Related Tweets Using Ensemble LSTM-GRU Model |
| title_fullStr | Sentiment Analysis and Emotion Detection on Cryptocurrency Related Tweets Using Ensemble LSTM-GRU Model |
| title_full_unstemmed | Sentiment Analysis and Emotion Detection on Cryptocurrency Related Tweets Using Ensemble LSTM-GRU Model |
| title_short | Sentiment Analysis and Emotion Detection on Cryptocurrency Related Tweets Using Ensemble LSTM-GRU Model |
| title_sort | sentiment analysis and emotion detection on cryptocurrency related tweets using ensemble lstm gru model |
| topic | Cryptocurrency sentiment analysis Text2Emotion emotion analysis machine learning |
| url | https://ieeexplore.ieee.org/document/9751065/ |
| work_keys_str_mv | AT nailaaslam sentimentanalysisandemotiondetectiononcryptocurrencyrelatedtweetsusingensemblelstmgrumodel AT furqanrustam sentimentanalysisandemotiondetectiononcryptocurrencyrelatedtweetsusingensemblelstmgrumodel AT ernestolee sentimentanalysisandemotiondetectiononcryptocurrencyrelatedtweetsusingensemblelstmgrumodel AT patrickbernardwashington sentimentanalysisandemotiondetectiononcryptocurrencyrelatedtweetsusingensemblelstmgrumodel AT imranashraf sentimentanalysisandemotiondetectiononcryptocurrencyrelatedtweetsusingensemblelstmgrumodel |